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Ne-te Duane Loh

Associate Professor Ne-te Duane Loh

Faculty & Department

Physics

Joint Appointments

Associate Professor, Biological Sciences, Science

Associate Professor, Integrative Sciences and Engineering

Jt Appt - Associate Professor, Biological Sciences, Science

Principal Investigator, Institute for Digital Molecular Analytics and Science (IDMxS)

Education

Doctor of Philosophy, Cornell University, New York, USA, United States

Not Available, Harvey Mudd College, USA, United States

Bio

Duane Loh is an Associate Professor in the Departments of Physics and Biological Sciences at the National University of Singapore (NUS) and a Principal Investigator at the NUS Centre for Bio-imaging Sciences. His research focuses on developing computational lenses, innovative tools that fuse machine learning with scientific and instrument priors to decode complex and chaotic dynamics at the nanometer scale.

Duane’s group pioneered these computational lenses for single-particle diffractive imaging using X-ray free-electron lasers, where they applied unsupervised learning to discover transient intermediate states and spontaneous order formation in highly dynamic systems. They extended these methods to electron-based imaging, overcoming challenges too complex for traditional hardware-based microscopy alone.

By combining advanced microscopy with statistical learning, Duane is leading efforts to explore nucleation processes, nanocrystal growth, and self-organization in both physical and biological systems. Building on their expertise in understanding complexity in physical systems,

Duane’s group is now applying these data-driven approaches to understanding the complex many-body dynamics of biological cells and the spread of vector-borne diseases. This work continues to bridge the gap between massive, complex datasets and foundational scientific understanding, pushing the boundaries of discovery in both physical and biological sciences.

Contact Information

Electron Microscopy
X-ray free-electron laser
Machine Learning
Data Science
Physics
Atomic Molecular and Optical Physics
AI, Machine Learning, Optoelectronics, Photonics, Optics, Semiconductors
Materials
Condensed Matter
Biophysics
Complex Systems
Complexity

Part of our research develop computational lenses, innovative tools that fuse machine learning with scientific and instrument priors to decode complex and chaotic dynamics at the nanometer scale.

We have pioneered these computational lenses for single-particle diffractive imaging using X-ray free-electron lasers, where they applied unsupervised learning to discover transient intermediate states and spontaneous order formation in highly dynamic systems. They extended these methods to electron-based imaging, overcoming challenges too complex for traditional hardware-based microscopy alone.

By combining advanced microscopy with statistical learning, Duane is leading efforts to explore nucleation processes, nanocrystal growth, and self-organization in both physical and biological systems. Building on their expertise in understanding complexity in physical systems,

We apply these data-driven approaches to understanding the complex many-body dynamics of biological cells and the spread of vector-borne diseases. This work continues to bridge the gap between massive, complex datasets and foundational scientific understanding, pushing the boundaries of discovery in both physical and biological sciences.